Intelligent Control Algorithm Research Articles

Experimental investigation and intelligent control of the magnus anti-rolling device for ship stability at zero speed

Under zero-speed conditions, ships are particularly susceptible to the effects of waves, which directly impact the safety of the vessel. A ship anti-rolling device based on the Magnus effect is designed to mitigate rolling motions across a full range of speeds, thereby enhancing the vessel's stability. This study presents an experimental investigation and intelligent control of Magnus anti-rolling devices aimed at enhancing ship stability at zero speed. The test setup, intelligent control algorithm, and experimental procedures specifically tailored for evaluating the Magnus anti-rolling device were designed. Following this, a comprehensive analysis was conducted to assess the effects of different cylinder geometries, swinging speeds, initial roll angles, and control methods on the anti-rolling characteristics of the device. Results demonstrate that the intelligent control method achieves an average anti-rolling efficiency of 89%. Additionally, the optimised geometric model of the Magnus anti-rolling device exhibits improved anti-rolling efficiency relative to the original model. The study confirms the stability and robustness of the intelligent Magnus anti-rolling device and suggests future research directions for practical applications aboard full-scale vessels in complex marine environments.

Intelligent control algorithm for dynamic positioning control system

Introduction: The dynamic positioning system resists the environmental forces such as wind, wave and current acting on the ship through the thruster, so that the ship can remain in the position required by the sea level as much as possible, and the operation is very convenient. But its current dynamic positioning ability can not meet people's needs.Methods: A Kalman filter based on untracked optimization was designed for dynamic positioning control system. Then the intelligent control algorithm is designed for the dynamic positioning top-level controller and thrust optimal distribution controller, which occupy an important position in the system, namely the adaptive weight variation particle swarm optimization algorithm and thrust optimal distribution algorithm.Results and Discussion: The average position error of three degrees of freedom after filter processing is 1.53 m. Compared with other mainstream controllers, the mean root error of controllers based on adaptive weight variation particle swarm optimization in environment A and B is 2.295 and 1.8 m, respectively. In environment C, the controller based on thrust optimization allocation algorithm can get the optimal solution when the full rotary thruster reaches the 7 s and the channel thruster reaches the 4 s. The thrust exclusion zone is crossed at 46 s in environment D. In the dynamic positioning capability curve of the system, the experimental hull can balance the different environmental loads at all angles. In summary, the intelligent control algorithm proposed in this paper can effectively improve the positioning ability of the dynamic positioning control system and meet the needs of people for ship navigation today.

Modular Intelligent Control System in the Pre-Assembly Stage

This paper presents a novel approach to developing fully automated intelligent control systems for use within production-based organizations, with a specific focus on advancing research into intelligent production systems. This analysis underscores a prevailing deficiency in control operations preceding assembly, where single-purpose control machines are commonly utilized, thus presenting inherent limitations. Conversely, while accurate multipurpose measurement centers exist, they often fail to deliver comprehensive quality control for manufactured parts due to cost and time constraints associated with the measuring process. The primary aim in this study was to develop an intelligent modular control system capable of overseeing the production of diverse components effectively. The modular intelligent control system is designed to meticulously monitor the quality of each module during the pre-assembly phase. By integrating sophisticated sensors, diagnostic tools, and intelligent control mechanisms, this system ensures precise control over module production processes. It facilitates the monitoring of multiple parameters and critical quality features, while integrated sensors and diagnostic methods promptly identify discrepancies and inaccuracies, enabling the swift diagnosis of issues within specific modules. The system’s intelligent control algorithms optimize production processes and ensure synchronization among individual modules, thereby ensuring consistent quality and performance. Notably, the implementation of this solution reduces inspection time by an average of 40 to 60% compared to manual inspection methods. Moreover, the system enables the comprehensive archiving of measurement data, eliminating the substantial error rates introduced by human involvement in the inspection process. Furthermore, the system enhances overall project efficiency, predictability, and safety, while allowing for rapid adjustments in order to meet standards and requirements. This innovative approach represents a significant advancement in intelligent control systems for use in production organizations, offering substantial benefits in terms of efficiency, accuracy, and adaptability.

Advanced intelligent optimization for electric vehicle charging stations integrated with photovoltaic system and battery storage in commercial buildings

This paper presents a sophisticated four-stage optimization and intelligent control algorithm tailored for two-way electric vehicle charging (EVC) stations integrated with advanced photovoltaic systems and fixed battery energy storage in commercial buildings. The primary objective is to minimize operating costs while prioritizing customer satisfaction within a dynamic and uncertain energy landscape. Our algorithm optimizes the scheduled charging and discharging of electric vehicles (EVs), local battery storage (BS) units, grid power supply, and deferred loads to balance instantaneous supply and demand. The first stage focuses on developing optimal energy management plans for the day ahead, considering factors such as projected energy production, anticipated EVC demand, and building energy consumption patterns. Building on this foundation, the second stage introduces multilayer EV charging price structures and optimizes participation rewards for discharging, dynamically addressing EV charging patterns and price sensitivities. Approaching the commissioning timeline, the third stage refines energy management plans for the upcoming hours using real-time data and forecasts, adapting to evolving conditions for optimal resource allocation. The final stage involves real-time control and the implementation of optimized programs, dynamically adjusting charge/discharge processes, grid interactions, and load deferral to maintain supply-demand balance and minimize operating costs. Our algorithm enhances system resilience in unpredictable conditions, providing compelling incentives for active EV user participation. Coordinating the integrated system efficiently, including the commercial building’s energy load, ensures reliable service to customers while reducing costs. Extensive case studies and a comparative analysis validate the algorithm’s efficiency in significantly reducing operating costs and enhancing resilience to uncertainty. The paper concludes by highlighting the algorithm’s pioneering role in intelligent EV charging station (CHS) management, offering a cost-effective, customer-oriented, and dynamic energy control strategy for advancing global energy practices.

ASCD: Automatic sensing and control device for crop irrigation scheduling

Traditional agricultural irrigation systems suffer from wasting a lot of water allocated for irrigation through evaporation and drainage. To address water waste and increase crop productivity, irrigation scheduling is used, as irrigation scheduling depends on four elements: soil moisture, soil characteristics, weather conditions, and plant type. This paper proposes a new design for monitoring agricultural conditions and controlling the amount of soil moisture by determining the required irrigation water ratios and the ideal time period for irrigating crops. An intelligent control and monitoring algorithm was created using experimental data for a wide range of plants (for soil moisture from 21 to 80 kPa), where plants were classified into three groups according to their common irrigation starting point. Based on the proposed plant classification, three models were provided, where irrigation ratios as the required parameter, soil moisture, plant type, and time interval as input parameters. This algorithm was used to program a custom-made automatic sensor and control device (ASCD). The ASCD is equipped with two input ports to communicate with two types of soil moisture sensors (a resistive sensor and a capacitive sensor) and with three output ports to drive three types of electronic irrigation valves. The ASCD can read from one sensor and drive one electronic valve at a time, the choice of which is made by the farmer. ASCD based the new algorithm showed high accuracy compared to a number of experimental results. The average absolute relative deviation (AAPD) of the new models and experimental data is 5.46 %. The application of the new algorithm shows a reduction in the amount of water used for crop irrigation during the day versus irrigation at night. On the other hand, ASCD has proven its success in sensing and controlling, and it works automatically and independently.

Heat exchanger control: Using deep-Q network

To achieve stable operation of district heating systems and improve the performance of heat exchange station control systems, this paper explores the application of intelligent control algorithms for controlling district heating systems. Initially, a heat exchanger neural network model is established. Secondly, a mass regulation controller based on a deep reinforcement learning algorithm is designed to accurately track the desired outlet temperature of the secondary side set point. Finally, the performance of the controller in terms of tracking response and disturbance suppression is verified using an example of a district heating system. The experimental results demonstrate that the controller exhibits minimal overshoot, with the outlet temperature of the secondary side deviating by less than 1 % when the controller setting value changes. Additionally, during disturbance suppression response, the deviation of the outlet temperature of the secondary side remains within ±2 °C. Compared to other controllers, the designed controller exhibits superior dynamic performance across various operating points and under different disturbances. Additionally, it outperforms traditional control methods in terms of stability and disturbance rejection capabilities.

Three-layer data center-based intelligent slice admission control algorithm for C-RAN using approximate reinforcement learning

Three-layer data center-based intelligent slice admission control algorithm for C-RAN using approximate reinforcement learning

Dynamic routing optimization in software-defined networking based on a metaheuristic algorithm

Optimizing resource allocation and routing to satisfy service needs is paramount in large-scale networks. Software-defined networking (SDN) is a new network paradigm that decouples forwarding and control, enabling dynamic management and configuration through programming, which provides the possibility for deploying intelligent control algorithms (such as deep reinforcement learning algorithms) to solve network routing optimization problems in the network. Although these intelligent-based network routing optimization schemes can capture network state characteristics, they are prone to falling into local optima, resulting in poor convergence performance. In order to address this issue, this paper proposes an African Vulture Routing Optimization (AVRO) algorithm for achieving SDN routing optimization. AVRO is based on the African Vulture Optimization Algorithm (AVOA), a population-based metaheuristic intelligent optimization algorithm with global optimization ability and fast convergence speed advantages. First, we improve the population initialization method of the AVOA algorithm according to the characteristics of the network routing problem to enhance the algorithm’s perception capability towards network topology. Subsequently, we add an optimization phase to strengthen the development of the AVOA algorithm and achieve stable convergence effects. Finally, we model the network environment, define the network optimization objective, and perform comparative experiments with the baseline algorithms. The experimental results demonstrate that the routing algorithm has better network awareness, with a performance improvement of 16.9% compared to deep reinforcement learning algorithms and 71.8% compared to traditional routing schemes.

Design and development of intelligent control strategies and algorithms for automated control of biotechnical objects under uncertainty

Uncertainty of environmental conditions is typical for most biological and agricultural systems. This uncertainty has recently become a serious challenge because of global climate changes. Due to delayed response to unpredictable environmental changes, the functioning of these systems is not always optimal. Another source of uncertainty is the adaptive response of a biological system, which usually does not coincide with the chosen objective function. This study focuses on developing intelligent control strategies to improve the performance of biotechnical systems under uncertainty. We use cause–effect diagrams to analyze the effect of uncertainties on the key performance indicators (KPIs). Our concept of intelligent control is based on the statistical evaluation of environmental uncertainties, while the decision-making is based on game theory. This intelligent control concept is universal and could be applied to different classes of biotechnical systems, from small animal farms to large industrial plants. Sugar production is considered as one of the objects of research, which requires minimizing sugar losses in four main unit operations: diffusion, purification, evaporation and crystallization. Each unit operation was modeled with the artificial neural networks (ANN) trained on the laboratory datasets. Based on simulated or real inputs from the technological process, ANNs predict outputs in terms of quality, losses, and energy efficiency. In stationary mode, ANNs are used to predict optimal technological regimes. They are used to correct technological regimes to maximize the objective function in real time. The application of intelligent algorithms improved process performance and energy efficiency.

Path planning of water surface garbage cleaning robot based on improved immune particle swarm algorithm

In order to effectively improve the efficiency of surface garbage cleaning robot, an intelligent control algorithm was applied to plan the robot path. To do so, an improved immune particle swarm algorithm was developed based on the robot model. This algorithm introduced the adaptive information dynamic adjustment strategy to dynamically adjust the main link indices, which improved the global searchability and convergence of particles and facilitated the quick identification of the optimal path by the robot. Through comparative simulation experiments with the particle swarm optimization algorithm, genetic algorithm, and immune particle swarm optimization algorithm, it was found that the robot based on the Adaptive Immune Particle Swarm Optimization (AIPSO) algorithm had the shortest planning path and search time, the lowest energy consumption, and the highest efficiency. A robot prototype platform was built. Compared to other algorithms, the efficiency of the robot space search based on the AIPSO algorithm was the highest, the search time was the shortest, and the energy consumption was also the lowest. Especially in the complex level 4 wave water environment, the AIPSO algorithm had the best adaptability and robustness, and the robot had the highest working efficiency and comprehensive performance. The experimental results revealed that the AIPSO algorithm effectively improved the path search and garbage cleaning efficiency of the robots and reduced the working time, which further verified the reliability and accuracy of the designed algorithm.

Intelligent residual film recovery machine monitoring and control system

Domestic residue recovery machines in China still follow the traditional towed mechanical model, lacking automation and intelligent features. Under the key R&D project of Xinjiang Autonomous Region, our team has developed integrated intelligent agricultural machinery for cotton straw crushing and residue recovery. This research covers the monitoring and control system, including PLC control and communication, working parameter monitoring and adjustment, algorithmic control of electro-hydraulic actuators, and human-machine interface display. Compared to traditional models, this machine replaces the conventional chain-driven mechanical structure and diesel tractor traction power with a fully hydraulic integrated suspension drive and power system. Sensors, PLC, and a touch screen are utilized for real-time monitoring and display of parameters. Hardware components like electromagnetic switch valves, electro-hydraulic proportional valves, hydraulic cylinders, and software elements such as PLC programs and intelligent control algorithms are employed for electro-hydraulic switching and proportional control of certain working parameters and mechanisms. All parameter information is consolidated in the PLC host and expansion modules and displayed and controlled on the human-machine interface. The system was installed on the first-generation machine for indoor testing, revealing that the monitoring and control system used on the first-generation machine achieves parameter detection and adjustment of the actuating mechanisms. It incorporates more advanced functions compared to traditional residue recovery machines. However, there is still ample room for improvement in the overall intelligence of the machine. The control system’s structure requires adjustment, and certain functionalities of the overall mechanical structure and working components need optimization or upgrading. Detailed enhancements to the entire system will be made in the next-generation machine.

Retracted: Automatic Intelligent Control System Based on Intelligent Control Algorithm

Retracted: Automatic Intelligent Control System Based on Intelligent Control Algorithm

Retracted: Electronic Controller Automatic Test System Based on Intelligent Control Algorithm

Retracted: Electronic Controller Automatic Test System Based on Intelligent Control Algorithm

Research on key technology and system application of energy management coupled with liquid cooling and intelligent control algorithm

With the continuous progress and innovation of science and technology, the demand and requirements for energy in modern society are getting higher and higher. The system application of liquid cooling and intelligent control algorithm coupling energy management can adapt to this demand and provide more efficient, accurate and intelligent energy management solutions. On the basis of a brief introduction to the current energy problems, the principle of liquid cooling technology and intelligent control algorithm are expounded, the design and key technologies of liquid cooling system and intelligent control algorithm are analyzed. The liquid cooling system based on fuzzy control is studied. The liquid cooling system based on fuzzy control can quickly and accurately respond to equipment temperature changes, avoid equipment damage due to overheating, and improve energy utilization efficiency and reduce energy waste. On the basis of a brief introduction to the current energy problems, this study describes the principle of liquid cooling technology and intelligent control algorithm, focuses on the design and key technologies of liquid cooling system and intelligent control algorithm, and studies the liquid cooling system based on fuzzy control. The liquid cooling system based on fuzzy control can quickly and accurately respond to equipment temperature changes, avoid equipment damage due to overheating, and can improve energy utilization efficiency and reduce energy waste.

Rancang Bangun Alat Pengendali Suhu pada Proses Pasteurisasi Susu Murni Menggunakan Arduino Berbasis IoT

This research aims to design and develop a temperature control device for the pasteurization process of pure milk using Arduino based on the Internet of Things (IoT). The device is designed to monitor and regulate the temperature automatically during the pasteurization process to ensure its quality and safety. Arduino, as the main platform, is used to collect temperature data through temperature sensors that are directly connected to the system. The temperature data is then sent to a server through the internet network for real-time processing and monitoring. Intelligent temperature control algorithms are implemented in Arduino to adjust heating or cooling during the pasteurization process according to the set temperature point. The results of this research are expected to provide an efficient and accurate solution for temperature control in the pasteurization process of pure milk, as well as improve the overall safety and quality of dairy products.

DERLight: A Deep Reinforcement Learning Traffic Light Control Algorithm with Dual Experience Replay

<p>In recent years, with the increasingly severe traffic environment, most cities are facing various traffic congestion problems, and the demand for intelligent regulation of traffic signals is also increasing. In this study, we propose a new intelligent traffic light control algorithm, dual experience replay light (DERLight), which innovatively and efficiently designs a dual experience replay training mechanism based on the classic deep Q network (DQN) framework and considers the dynamic epoch function. As results show that compared with some state-of-the-art algorithms, DERLight can shorten the average travel time of vehicles, increase the throughput at intersections, and also speed up the convergence of the network. In addition, the design of this algorithm framework is not only limited to the field of intelligent transportation, but also has transferability for some other fields.</p> <p> </p>

Research on intelligent vehicle Traffic Flow control algorithm based on data mining

Traffic Congestion (TC) is increasing due to urban growth and vehicle numbers, rendering the development of cities and people's well-being difficult. Traffic Prediction (TP) and control systems have been required to improve Traffic Flow (TF) and reduce TC because standard methods are unsuitable. The paper proposes an innovative method for traffic control using the Dynamic Zone Segmentation Algorithm (DZSA) to solve this significant issue. The algorithm uses real-time data and road conditions to partition city traffic into manageable units, enhancing the adaptability and accuracy of Traffic Prediction (TP) performance. Applying DZSA, the recommended Long Short-Term Memory + Bayesian Structural Time Series (LSTM + BSTS) learning model optimizes TP by integrating the best features of conventional and Machine Learning (ML) methods. The model optimized quality performance when experimentally tested against other benchmark models using metrics like Mean Absolute Error, Mean Absolute Scaled Error, Accuracy Percent, Root Mean Squared Error, and Mean Absolute Percent Error. The recommended model, LSTM + BSTS, shows a minimal error rate of 6.68%, indicating its success.

Research on intelligent active disturbance rejection control algorithm for shock train leading edge of dual-mode scramjet

To prevent scramjet inlet unstart, stringent control over the shock train's leading edge in the isolator is necessary. Therefore, this study proposes a fuel flow active control scheme combining an adaptive particle swarm optimization algorithm (APSO) and active disturbance rejection control (ADRC) to enhance the traditional control (such as proportional-integral-derivative, PID) system's stability and anti-interference capabilities in complex and uncertain environments. Within this context, a simple, semi-empirical mathematical model of a dual-mode scramjet is constructed. An ADRC controller is designed to counteract the nonlinearity and disturbance of the dual-mode scramjet. ADRC, not requiring an accurate mathematical model, utilizes its extended state observer to estimate all uncertain factors affecting the controlled object based on the relationship between the shock train's leading edge and the controller's output, treating them as unknown disturbances for compensation, thereby exhibiting strong anti-interference capabilities. In order to optimize the performance, the APSO is introduced to fine-tune the ADRC controller parameters. The APSO reduces the rise time of ADRC from 22.28 to 11.39 ms, and the adjustment time from 40.05 to 13.64 ms. The APSO effectively tunes the parameters of the ADRC controller, which ensures rapidity comparable to the PID and all-coefficient adaptive control (ACAC) in managing the shock train's leading edge. At the same time, ADRC has stronger anti-interference ability and adaptive ability than ACAC and PID. This research preliminarily verifies the feasibility and advancement of the ADRC for shock train leading-edge control in scramjet isolators.

Intelligent Control of Electric Vehicle Drives using Swarm Robotics

This study investigates the incorporation of swarm robotics into the control mechanism of electric vehicles (EVs), introducing an innovative intelligent control framework that utilizes the concepts of decentralized decision-making. The research entails a methodical inquiry that encompasses the design of system architecture, the creation of a model for swarm robotics, the modeling of electric vehicle drive, the integration of swarm robotics with EV control, the development of algorithms for intelligent control, and the execution of real-world tests. The fleet of electric cars, propelled by a collective of independent robotic entities, displayed remarkable flexibility in adjusting to fluctuating surroundings. Findings demonstrated disparities in operating duration, distance traversed, mean speed, and energy expenditure during several iterations, highlighting the system’s adeptness in promptly reacting to instantaneous inputs. Significantly, the swarm-propelled electric cars successfully attained varied operating durations, showcasing the system’s adaptability in accommodating environmental dynamics. The swarm-driven system demonstrated its navigation effectiveness by effectively covering various distances, highlighting its versatility and extensive coverage capabilities. The system’s ability to effectively balance energy economy and performance is shown by the collective regulation of average velocity. The energy consumption study demonstrated the system’s efficacy in optimizing energy use, with certain experiments showing significant savings. Percentage change studies have yielded valuable insights into the comparative enhancements or difficulties seen in each indicator, so illustrating the influence of decentralized decision-making on operational results. This study is a valuable contribution to the ever-changing field of intelligent transportation systems, providing insight into the immense potential of swarm-driven electric cars to completely transform sustainable and adaptable transportation. The results highlight the remarkable flexibility and optimization skills of swarm robotics in the management of electric vehicles, paving the way for future advancements in the quest for intelligent, energyefficient, and dynamically responsive transportation solutions.

Modeling of biotechnological objects

The article discusses the system of modeling biotechnological objects and biogas production in the process of fermentation of various agricultural wastes. The complexity of automation of biotechnological processes, which consists in the lack of sufficient knowledge about the phenomena associated with the process of fermentation and the synthesis of target materials, is shown. Solutions to control problems using intelligent methods, intelligent control systems, based on fuzzy logic and on the basis of on the basis of neural network technologies, which have proven themselves in the control of complex objects with parameters that vary widely, as well as the analysis of algorithms for the operation of programmable logic controllers that perform the functions of regulators in the systems of operational control of complex processes, adaptive neuro-fuzzy architecture. Distributed in management decision support systems, it is made in the form of a six-layer neural structure, i.e. systems with neuro-fuzzy regulators, are able to largely meet the modern requirements of the designed systems, as a result of which the systems, using the technology of synthesis of intelligent control algorithms, can optimize complex circuits of automatic control systems.